ML_PQ_Constituency_Service_NB.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Notebook for using machine learning and parliamentary questions to measure politicians' constituency service\n",
    "\n",
    "This notebook contains the code for the analysis presented in my Using Machine Learning and Parliamentary Questions to Measure Politicians' Constituency Service blog post."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import the required libraries and functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# numpy, pandas, counter\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from collections import Counter\n",
    "# matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.backends.backend_pdf\n",
    "# seaborn\n",
    "import seaborn as sns\n",
    "# Scikit-learn\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.model_selection import GridSearchCV, train_test_split, KFold, cross_val_predict\n",
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import MultinomialNB, BernoulliNB\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.preprocessing import label_binarize\n",
    "from sklearn.metrics import accuracy_score, classification_report, f1_score, make_scorer\n",
    "# wordcloud\n",
    "from wordcloud import WordCloud, STOPWORDS\n",
    "%matplotlib inline\n",
    "plt.rcParams[\"axes.labelsize\"] = 15\n",
    "plt.rcParams[\"ytick.labelsize\"] = 'large'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "labelled = pd.read_csv('labelled.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check that data was imported correctly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>filename</th>\n",
       "      <th>date</th>\n",
       "      <th>speaker</th>\n",
       "      <th>X</th>\n",
       "      <th>Name</th>\n",
       "      <th>Party</th>\n",
       "      <th>Constituency.x</th>\n",
       "      <th>Female</th>\n",
       "      <th>Dynastic</th>\n",
       "      <th>Votes</th>\n",
       "      <th>Vote_share</th>\n",
       "      <th>Quota</th>\n",
       "      <th>ff</th>\n",
       "      <th>gov</th>\n",
       "      <th>docs</th>\n",
       "      <th>dail</th>\n",
       "      <th>ID_unit</th>\n",
       "      <th>Stratum</th>\n",
       "      <th>Local</th>\n",
       "      <th>labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>/gpfs/group/dynrr/ireland/xml_by_session/20/xm...</td>\n",
       "      <td>19760701</td>\n",
       "      <td>644</td>\n",
       "      <td>425</td>\n",
       "      <td>Bill Loughnane</td>\n",
       "      <td>Fianna Fail</td>\n",
       "      <td>Clare Galway South</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6956</td>\n",
       "      <td>26.46</td>\n",
       "      <td>1.06</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>asked the minister for foreign affairs if he w...</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>not</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>/gpfs/group/dynrr/ireland/xml_by_session/20/xm...</td>\n",
       "      <td>19760701</td>\n",
       "      <td>644</td>\n",
       "      <td>425</td>\n",
       "      <td>Bill Loughnane</td>\n",
       "      <td>Fianna Fail</td>\n",
       "      <td>Clare Galway South</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6956</td>\n",
       "      <td>26.46</td>\n",
       "      <td>1.06</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>asked the minister for foreign affairs if he i...</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>not</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>/gpfs/group/dynrr/ireland/xml_by_session/20/xm...</td>\n",
       "      <td>19760701</td>\n",
       "      <td>544</td>\n",
       "      <td>414</td>\n",
       "      <td>Thomas Hussey</td>\n",
       "      <td>Fianna Fail</td>\n",
       "      <td>Galway North East</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5340</td>\n",
       "      <td>20.37</td>\n",
       "      <td>0.81</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>asked the minister for industry and commerce t...</td>\n",
       "      <td>20</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>local</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>/gpfs/group/dynrr/ireland/xml_by_session/20/xm...</td>\n",
       "      <td>19760325</td>\n",
       "      <td>754</td>\n",
       "      <td>237</td>\n",
       "      <td>Thomas Meaney</td>\n",
       "      <td>Fianna Fail</td>\n",
       "      <td>Cork Mid</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10400</td>\n",
       "      <td>25.64</td>\n",
       "      <td>1.28</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>asked the minister for agriculture and fisheri...</td>\n",
       "      <td>20</td>\n",
       "      <td>154</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>local</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>/gpfs/group/dynrr/ireland/xml_by_session/20/xm...</td>\n",
       "      <td>19760205</td>\n",
       "      <td>797</td>\n",
       "      <td>585</td>\n",
       "      <td>Ciaran Murphy</td>\n",
       "      <td>Fianna Fail</td>\n",
       "      <td>Wicklow</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4521</td>\n",
       "      <td>15.31</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>asked the minister for education the grants to...</td>\n",
       "      <td>20</td>\n",
       "      <td>252</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>not</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            filename      date  speaker    X  \\\n",
       "0  /gpfs/group/dynrr/ireland/xml_by_session/20/xm...  19760701      644  425   \n",
       "1  /gpfs/group/dynrr/ireland/xml_by_session/20/xm...  19760701      644  425   \n",
       "2  /gpfs/group/dynrr/ireland/xml_by_session/20/xm...  19760701      544  414   \n",
       "3  /gpfs/group/dynrr/ireland/xml_by_session/20/xm...  19760325      754  237   \n",
       "4  /gpfs/group/dynrr/ireland/xml_by_session/20/xm...  19760205      797  585   \n",
       "\n",
       "             Name        Party      Constituency.x  Female  Dynastic  Votes  \\\n",
       "0  Bill Loughnane  Fianna Fail  Clare Galway South       0         0   6956   \n",
       "1  Bill Loughnane  Fianna Fail  Clare Galway South       0         0   6956   \n",
       "2   Thomas Hussey  Fianna Fail   Galway North East       0         0   5340   \n",
       "3   Thomas Meaney  Fianna Fail            Cork Mid       0         1  10400   \n",
       "4   Ciaran Murphy  Fianna Fail             Wicklow       0         0   4521   \n",
       "\n",
       "   Vote_share  Quota  ff  gov  \\\n",
       "0       26.46   1.06   1    0   \n",
       "1       26.46   1.06   1    0   \n",
       "2       20.37   0.81   1    0   \n",
       "3       25.64   1.28   1    0   \n",
       "4       15.31   0.61   1    0   \n",
       "\n",
       "                                                docs  dail  ID_unit  Stratum  \\\n",
       "0  asked the minister for foreign affairs if he w...    20        6        1   \n",
       "1  asked the minister for foreign affairs if he i...    20        8        1   \n",
       "2  asked the minister for industry and commerce t...    20       22        1   \n",
       "3  asked the minister for agriculture and fisheri...    20      154        1   \n",
       "4  asked the minister for education the grants to...    20      252        1   \n",
       "\n",
       "   Local labels  \n",
       "0      0    not  \n",
       "1      0    not  \n",
       "2      1  local  \n",
       "3      1  local  \n",
       "4      0    not  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labelled.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get the question and label columns for text classification model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = labelled['docs']\n",
    "y = labelled[['labels']].values.flatten()\n",
    "y = label_binarize(y, classes=['not', 'local']).ravel()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare data for analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=1253) #split data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "count_vect = CountVectorizer(decode_error='ignore')\n",
    "X_train_counts = count_vect.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 5,913 words in the vocabulary.\n",
      "(2000,)\n",
      "(2000, 5913)\n",
      "2000\n",
      "Counter({0: 1196, 1: 804})\n"
     ]
    }
   ],
   "source": [
    "print \"There are {:,} words in the vocabulary.\".format(len(count_vect.vocabulary_))\n",
    "print X_train.shape\n",
    "print len(X_train)\n",
    "print Counter(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_test_counts = count_vect.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(500, 5913)\n",
      "500\n",
      "Counter({0: 299, 1: 201})\n"
     ]
    }
   ],
   "source": [
    "print X_test_counts.shape\n",
    "print len(X_test)\n",
    "print Counter(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use cross validation on training set to find optimal model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "kf = KFold(n_splits=10, shuffle=True, random_state=1213)\n",
    "f1_scorer = make_scorer(f1_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Logistic Regression with word counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1-score: 0.885 (+/-0.036) for {'penalty': 'l1', 'C': 1}\n",
      "F1-score: 0.884 (+/-0.039) for {'penalty': 'l2', 'C': 1}\n",
      "F1-score: 0.885 (+/-0.034) for {'penalty': 'l1', 'C': 0.9}\n",
      "F1-score: 0.884 (+/-0.041) for {'penalty': 'l2', 'C': 0.9}\n",
      "F1-score: 0.883 (+/-0.034) for {'penalty': 'l1', 'C': 0.8}\n",
      "F1-score: 0.883 (+/-0.042) for {'penalty': 'l2', 'C': 0.8}\n",
      "F1-score: 0.885 (+/-0.035) for {'penalty': 'l1', 'C': 0.7}\n",
      "F1-score: 0.882 (+/-0.042) for {'penalty': 'l2', 'C': 0.7}\n",
      "F1-score: 0.883 (+/-0.040) for {'penalty': 'l1', 'C': 0.6}\n",
      "F1-score: 0.884 (+/-0.044) for {'penalty': 'l2', 'C': 0.6}\n",
      "F1-score: 0.881 (+/-0.047) for {'penalty': 'l1', 'C': 0.5}\n",
      "F1-score: 0.886 (+/-0.041) for {'penalty': 'l2', 'C': 0.5}\n",
      "F1-score: 0.875 (+/-0.046) for {'penalty': 'l1', 'C': 0.25}\n",
      "F1-score: 0.878 (+/-0.046) for {'penalty': 'l2', 'C': 0.25}\n",
      "F1-score: 0.869 (+/-0.063) for {'penalty': 'l1', 'C': 0.1}\n",
      "F1-score: 0.872 (+/-0.038) for {'penalty': 'l2', 'C': 0.1}\n"
     ]
    }
   ],
   "source": [
    "grid = {\n",
    "        'C': [1, .9, .8, .7, .6, .5, .25, .1],\n",
    "        'penalty' : ['l1', 'l2']\n",
    "    }\n",
    "logit = LogisticRegression()\n",
    "loggs = GridSearchCV(logit, grid, scoring=f1_scorer, cv=kf)\n",
    "loggs.fit(X_train_counts, y_train)\n",
    "means = loggs.cv_results_['mean_test_score']\n",
    "stds = loggs.cv_results_['std_test_score']\n",
    "for mean, std, params in zip(means, stds, loggs.cv_results_['params']):\n",
    "    print(\"F1-score: %0.3f (+/-%0.03f) for %r\" % (mean, std * 2, params))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Multinomial Naive Bayes with counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1-score: 0.863 (+/-0.035) for {'alpha': 1}\n",
      "F1-score: 0.861 (+/-0.036) for {'alpha': 0.8}\n",
      "F1-score: 0.859 (+/-0.035) for {'alpha': 0.6}\n",
      "F1-score: 0.858 (+/-0.041) for {'alpha': 0.4}\n",
      "F1-score: 0.856 (+/-0.052) for {'alpha': 0.2}\n"
     ]
    }
   ],
   "source": [
    "grid = {\n",
    "        'alpha': [1, .8,.6, .4, .2]\n",
    "    }\n",
    "mnb = MultinomialNB()\n",
    "mnbgs = GridSearchCV(mnb, grid, scoring=f1_scorer, cv=kf)\n",
    "mnbgs.fit(X_train_counts, y_train)\n",
    "means = mnbgs.cv_results_['mean_test_score']\n",
    "stds = mnbgs.cv_results_['std_test_score']\n",
    "for mean, std, params in zip(means, stds, mnbgs.cv_results_['params']):\n",
    "    print(\"F1-score: %0.3f (+/-%0.03f) for %r\" % (mean, std * 2, params))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Support vector machine with counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1-score: 0.870 (+/-0.038) for {'C': 1}\n",
      "F1-score: 0.871 (+/-0.036) for {'C': 0.9}\n",
      "F1-score: 0.872 (+/-0.035) for {'C': 0.8}\n",
      "F1-score: 0.872 (+/-0.038) for {'C': 0.7}\n",
      "F1-score: 0.874 (+/-0.041) for {'C': 0.6}\n",
      "F1-score: 0.876 (+/-0.043) for {'C': 0.5}\n"
     ]
    }
   ],
   "source": [
    "grid = {\n",
    "        'C': [1, .9, .8, .7, .6, .5]\n",
    "    }\n",
    "svc = SVC(kernel='linear')\n",
    "svcgs = GridSearchCV(svc, grid, scoring=f1_scorer, cv=kf)\n",
    "svcgs.fit(X_train_counts, y_train)\n",
    "means = svcgs.cv_results_['mean_test_score']\n",
    "stds = svcgs.cv_results_['std_test_score']\n",
    "for mean, std, params in zip(means, stds, svcgs.cv_results_['params']):\n",
    "    print(\"F1-score: %0.3f (+/-%0.03f) for %r\" % (mean, std * 2, params))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate test performance for each optimal model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.90      0.95      0.93       299\n",
      "          1       0.92      0.84      0.88       201\n",
      "\n",
      "avg / total       0.91      0.91      0.91       500\n",
      "\n",
      "0.908\n"
     ]
    }
   ],
   "source": [
    "logpreds = loggs.predict(X_test_counts)\n",
    "print(classification_report(y_test, logpreds))\n",
    "print accuracy_score(y_test, logpreds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.90      0.92      0.91       299\n",
      "          1       0.88      0.85      0.86       201\n",
      "\n",
      "avg / total       0.89      0.89      0.89       500\n",
      "\n",
      "0.908\n"
     ]
    }
   ],
   "source": [
    "mnbpreds = mnbgs.predict(X_test_counts)\n",
    "print(classification_report(y_test, mnbpreds))\n",
    "print accuracy_score(y_test, logpreds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.90      0.92      0.91       299\n",
      "          1       0.88      0.85      0.87       201\n",
      "\n",
      "avg / total       0.89      0.89      0.89       500\n",
      "\n",
      "0.908\n"
     ]
    }
   ],
   "source": [
    "svcpreds = svcgs.predict(X_test_counts)\n",
    "print(classification_report(y_test, svcpreds))\n",
    "print accuracy_score(y_test, logpreds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get most important features in logit model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_most_informative_features(vectorizer, clf, out=0, n=5):\n",
    "    feature_names = vectorizer.get_feature_names()\n",
    "    coefs_with_fns = sorted(zip(clf.best_estimator_.coef_[0], feature_names))\n",
    "    top = coefs_with_fns[:n] + coefs_with_fns[:-(n + 1):-1]\n",
    "    df = pd.DataFrame(top, columns=['Coefficient', 'Word'])\n",
    "    df = df.sort_values(by = 'Coefficient')\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "top_features = get_most_informative_features(vectorizer=count_vect, clf=loggs, out=0, n=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ezKZNm8osXHn56quvCAkJwd3dHYCwsDBOnToFgL+/vyOjiYjI/ytRwXJ1dSUlJaXQ8h9+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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11243c710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"whitegrid\")\n",
    "ax = sns.barplot(y=\"Word\", x=\"Coefficient\", data=top_features, palette=\"RdBu_r\")\n",
    "figure = ax.get_figure()    \n",
    "figure.savefig(\"coefplot.png\", dpi=350)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import unlabelled data\n",
    "\n",
    "Note that I that I am not releasing this data as it is employed in ongoing research projects. Therefore it will not be possible to reproduce the analysis below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(305804, 5913)\n"
     ]
    }
   ],
   "source": [
    "unlabelled = pd.read_csv('unlabelled.csv')\n",
    "X_unlabelled_counts = count_vect.transform(unlabelled['docs'])\n",
    "print X_unlabelled_counts.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>filename</th>\n",
       "      <th>date</th>\n",
       "      <th>speaker</th>\n",
       "      <th>dail</th>\n",
       "      <th>X</th>\n",
       "      <th>Name</th>\n",
       "      <th>Party</th>\n",
       "      <th>Constituency.x</th>\n",
       "      <th>Female</th>\n",
       "      <th>Dynastic</th>\n",
       "      <th>Votes</th>\n",
       "      <th>Vote_share</th>\n",
       "      <th>Quota</th>\n",
       "      <th>ff</th>\n",
       "      <th>gov</th>\n",
       "      <th>docs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>/gpfs/group/dynrr/ireland/xml_by_session/20/xm...</td>\n",
       "      <td>19760701</td>\n",
       "      <td>906</td>\n",
       "      <td>20</td>\n",
       "      <td>454</td>\n",
       "      <td>Michael O'Kennedy</td>\n",
       "      <td>Fianna Fail</td>\n",
       "      <td>Tipperary North</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5709</td>\n",
       "      <td>20.14</td>\n",
       "      <td>0.81</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>and ruairí brugha asked the minister for forei...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>/gpfs/group/dynrr/ireland/xml_by_session/20/xm...</td>\n",
       "      <td>19760701</td>\n",
       "      <td>906</td>\n",
       "      <td>20</td>\n",
       "      <td>454</td>\n",
       "      <td>Michael O'Kennedy</td>\n",
       "      <td>Fianna Fail</td>\n",
       "      <td>Tipperary North</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5709</td>\n",
       "      <td>20.14</td>\n",
       "      <td>0.81</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>asked the minister for foreign affairs if the ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>/gpfs/group/dynrr/ireland/xml_by_session/20/xm...</td>\n",
       "      <td>19760701</td>\n",
       "      <td>313</td>\n",
       "      <td>20</td>\n",
       "      <td>385</td>\n",
       "      <td>Barry Desmond</td>\n",
       "      <td>Labour Party</td>\n",
       "      <td>Dun Laoghaire and Rathdown</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5768</td>\n",
       "      <td>13.73</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>asked the minister for foreign affairs if he w...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>/gpfs/group/dynrr/ireland/xml_by_session/20/xm...</td>\n",
       "      <td>19760701</td>\n",
       "      <td>198</td>\n",
       "      <td>20</td>\n",
       "      <td>344</td>\n",
       "      <td>Edward Collins</td>\n",
       "      <td>Fine Gael</td>\n",
       "      <td>Waterford</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6719</td>\n",
       "      <td>21.78</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>asked the minister for foreign affairs if he i...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>/gpfs/group/dynrr/ireland/xml_by_session/20/xm...</td>\n",
       "      <td>19760701</td>\n",
       "      <td>768</td>\n",
       "      <td>20</td>\n",
       "      <td>241</td>\n",
       "      <td>Bobby Molloy</td>\n",
       "      <td>Fianna Fail</td>\n",
       "      <td>Galway West</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7028</td>\n",
       "      <td>28.31</td>\n",
       "      <td>1.13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>asked the minister for foreign affairs if he h...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            filename      date  speaker  dail  \\\n",
       "0  /gpfs/group/dynrr/ireland/xml_by_session/20/xm...  19760701      906    20   \n",
       "1  /gpfs/group/dynrr/ireland/xml_by_session/20/xm...  19760701      906    20   \n",
       "2  /gpfs/group/dynrr/ireland/xml_by_session/20/xm...  19760701      313    20   \n",
       "3  /gpfs/group/dynrr/ireland/xml_by_session/20/xm...  19760701      198    20   \n",
       "4  /gpfs/group/dynrr/ireland/xml_by_session/20/xm...  19760701      768    20   \n",
       "\n",
       "     X               Name         Party              Constituency.x  Female  \\\n",
       "0  454  Michael O'Kennedy   Fianna Fail             Tipperary North       0   \n",
       "1  454  Michael O'Kennedy   Fianna Fail             Tipperary North       0   \n",
       "2  385      Barry Desmond  Labour Party  Dun Laoghaire and Rathdown       0   \n",
       "3  344     Edward Collins     Fine Gael                   Waterford       0   \n",
       "4  241       Bobby Molloy   Fianna Fail                 Galway West       0   \n",
       "\n",
       "   Dynastic  Votes  Vote_share  Quota  ff  gov  \\\n",
       "0         0   5709       20.14   0.81   1    0   \n",
       "1         0   5709       20.14   0.81   1    0   \n",
       "2         0   5768       13.73   0.69   0    1   \n",
       "3         0   6719       21.78   0.87   0    1   \n",
       "4         0   7028       28.31   1.13   1    0   \n",
       "\n",
       "                                                docs  \n",
       "0  and ruairí brugha asked the minister for forei...  \n",
       "1  asked the minister for foreign affairs if the ...  \n",
       "2  asked the minister for foreign affairs if he w...  \n",
       "3  asked the minister for foreign affairs if he i...  \n",
       "4  asked the minister for foreign affairs if he h...  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unlabelled.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate word cloud using the text from the unlabelled corpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "text = ' '.join(unlabelled['docs'].tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "stopwords = set(STOPWORDS)\n",
    "stopwords.add(\"minister\")\n",
    "stopwords.add(\"asked\")\n",
    "stopwords.add(\"statement\")\n",
    "stopwords.add(\"make\")\n",
    "stopwords.add(\"will\")\n",
    "stopwords.add(\"matter\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "wordcloud = WordCloud(background_color=\"white\", max_words=2000, stopwords = stopwords,\n",
    "                      max_font_size=40, random_state=42).generate(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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N+JV8nJKbStdsB64oiNR4NlLhWjtre1wL0xVvpsBRSVgdBSCmhehPnGVb7gNT/dzBas8m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WBmZRmLwcB4KoXFaGQ0mWBD6eW90G83HDYLFrOJ3Yc6ed8tq1AUlVxeJpvTg02iKCKKAndtW87J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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1251dde90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(wordcloud, interpolation='bilinear')\n",
    "plt.axis(\"off\")\n",
    "plt.savefig('qs_wordcloud.png', dpi=650)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot number of questions submitted over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZwujRoylZsiRz5syhWLFifkfK0VIq9IGeRhsJDBaRMzd2ikg+3NFdoVx2YAzu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\ndcEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1126b3150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "doccounts = unlabelled.groupby('dail')['dail'].count()\n",
    "doccounts = pd.DataFrame({'Dail':doccounts.index, 'Questions':doccounts.values})\n",
    "ax = sns.pointplot(x=\"Dail\", y=\"Questions\", data=doccounts, sharex=False, sharey=False, color='black')\n",
    "ax.set(ylim=(0, None))\n",
    "ax.set(xlabel='Dail Eireann', ylabel='Questions submitted by legislators')\n",
    "figure = ax.get_figure()    \n",
    "figure\n",
    "figure.savefig(\"qstimeplot.png\", dpi=350, bbox_inches='tight')"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}